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NumPy Cheat Sheet

NumPy is the fundamental package for scientific computing with Python.

This cheat sheet acts as a intro to Python for data science. Contact me here for typos or suggestions, and - of course - fork and tune it to your taste!

Index

  1. Basics
  2. Arrays
  3. Mathematics
  4. Slicing and Subsetting
  5. Tricks
  6. Credits

Basics

One of the most commonly used functions of NumPy are NumPy arrays: The essential difference between lists and NumPy arrays is functionality and speed. lists give you basic operation, but NumPy adds FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc.
The most important difference for data science is the ability to do element-wise calculations with NumPy arrays.

axis 0 always refers to row
axis 1 always refers to column

Operator Description Documentation
np.array([1,2,3]) 1d array link
np.array([(1,2,3),(4,5,6)]) 2d array see above
np.arange(start,stop,step) range array link

Placeholders

Operators Description Documentation
np.linspace(0,2,9) Add evenly spaced values btw interval to array of length link
np.zeros((1,2)) Create and array filled with zeros link
np.ones((1,2)) Creates an array filled with ones link
np.random.random((5,5)) Creates random array link
np.empty((2,2)) Creates an empty array link

Examples 

# 1 dimensional
x = np.array([1,2,3])
# 2 dimensional
y = np.array([(1,2,3),(4,5,6)])

x = np.arange(3)
>>> array([0, 1, 2])

y = np.arange(3.0)
>>> array([ 0.,  1.,  2.])

x = np.arange(3,7)
>>> array([3, 4, 5, 6])

y = np.arange(3,7,2)
>>> array([3, 5])

Array

Array Properties

Syntax Description Documentation
array.shape Dimensions (Rows,Columns) link
len(array) Length of Array link
array.ndim Number of Array Dimensions link
array.size Number of Array Elements link
array.dtype Data Type link
array.astype(type) Converts to Data Type link
type(array) Type of Array link

Copying/Sorting

Operators Descriptions Documentation
np.copy(array) Creates copy of array link
other = array.copy() Creates deep copy of array see above
array.sort() Sorts an array link
array.sort(axis=0) Sorts axis of array see above

Examples 

# Sort sorts in ascending order
y = np.array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
y.sort()
print(y)
>>> [ 1  2  3  4  5  6  7  8  9  10]

Array Manipulation Routines

Adding or Removing Elements

Operator Description Documentation
np.append(a,b) Append items to array link
np.insert(array, 1, 2, axis) Insert items into array at axis 0 or 1 link
array.resize((2,4)) Resize array to shape(2,4) link
np.delete(array,1,axis) Deletes items from array link

Combining Arrays

Operator Description Documentation
np.concatenate((a,b),axis=0) Concatenates 2 arrays, adds to end link
np.vstack((a,b)) Stack array row-wise link
np.hstack((a,b)) Stack array column wise link

Splitting Arrays

Operator Description Documentation
numpy.split() link
np.array_split(array, 3) Split an array in sub-arrays of (nearly) identical size link
numpy.hsplit(array, 3) Split the array horizontally at 3rd index link

More

Operator Description Documentation
other = ndarray.flatten() Flattens a 2d array to 1d link
array = np.transpose(other)
array.T		 Transpose array link

Mathematics

Operations

Operator Description Documentation
np.add(x,y) Addition link
np.substract(x,y) Subtraction link
np.divide(x,y) Division link
np.multiply(x,y) Multiplication link
np.sqrt(x) Square Root link
np.sin(x) Element-wise sine link
np.cos(x) Element-wise cosine link
np.log(x) Element-wise natural log link
np.dot(x,y) Dot product link

Remember: NumPy array operations work element-wise.

Example 

# If a 1d array is added to a 2d array (or the other way), NumPy
# chooses the array with smaller dimension and adds it to the one
# with bigger dimension
a = np.array([1, 2, 3])
b = np.array([(1, 2, 3), (4, 5, 6)])
print(np.add(a, b))
>>> [[2 4 6]
     [5 7 9]]

Comparison

Operator Description Documentation
== Equal link
!= Not equal link
< Smaller than link
> Greater than link
<= Smaller than or equal link
>= Greater than or equal link
np.array_equal(x,y) Array-wise comparison link

Example 

# Using comparison operators will create boolean NumPy arrays
z = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
c = z < 6
print(c)
>>> [ True  True  True  True  True False False False False False]

Basic Statistics

Operator Description Documentation
array.mean()
np.mean(array)		 Mean link
np.median(array) Median link
array.corrcoef() Correlation Coefficient link
array.std(array) Standard Deviation link

More

Operator Description Documentation
array.sum() Array-wise sum link
array.min() Array-wise minimum value link
array.max(axis=0) Maximum value of specified axis
array.cumsum(axis=0) Cumulative sum of specified axis link

Slicing and Subsetting

Operator Description Documentation
array[i] 1d array at index i link
array[i,j] 2d array at index[i][j] see above
array[i<4] Boolean Indexing, see Tricks see above
array[0:3] Select items of index 0, 1 and 2 see above
array[0:2,1] Select items of rows 0 and 1 at column 1 see above
array[:1] Select items of row 0 (equals array[0:1, :]) see above
array[1:2, :] Select items of row 1 see above
[comment]: <> ( array[1,...] equals array[1,:,:]
array[ : :-1] Reverses array see above

Examples <a name"exp">

b = np.array([(1, 2, 3), (4, 5, 6)])

# The index *before* the comma refers to *rows*,
# the index *after* the comma refers to *columns*
print(b[0:1, 2])
>>> [3]

print(b[:len(b), 2])
>>> [3 6]

print(b[0, :])
>>> [1 2 3]

print(b[0, 2:])
>>> [3]

print(b[:, 0])
>>> [1 4]

c = np.array([(1, 2, 3), (4, 5, 6)])
d = c[1:2, 0:2]
print(d)
>>> [[4 5]]

Tricks

This is a growing list of examples. Know a good trick? Let me know here or fork it and create a pull request.

boolean indexing (available as separate .py file here

# Index trick when working with two np-arrays
a = np.array([1,2,3,6,1,4,1])
b = np.array([5,6,7,8,3,1,2])

# Only saves a at index where b == 1
other_a = a[b == 1]
#Saves every spot in a except at index where b != 1
other_other_a = a[b != 1]
x = np.array([4,6,8,1,2,6,9])
y = x > 5
print(x[y])
>>> [6 8 6 9]

# Even shorter
x = np.array([1, 2, 3, 4, 4, 35, 212, 5, 5, 6])
print(x[x < 5])
>>> [1 2 3 4 4]

Credits

Datacamp, Quandl & Official docs